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Kmean.py
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Kmean.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Nov 26 02:31:50 2017
@author: hammadkhan
"""
import numpy as np
import os
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_score, silhouette_samples
from pyclust import BisectKMeans
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy.sparse import hstack
import re
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
def purity_score(clusters, classes):
"""
Calculate the purity score for the given cluster assignments and ground truth classes
:param clusters: the cluster assignments array
:type clusters: numpy.array
:param classes: the ground truth classes
:type classes: numpy.array
:returns: the purity score
:rtype: float
"""
A = np.c_[(clusters,classes)]
print((A[0:5]))
n_accurate = 0.
for j in np.unique(A[:,0]):
z = A[A[:,0] == j, 1]
x = np.argmax(np.bincount(z))
n_accurate += len(z[z == x])
return n_accurate / A.shape[0]
# Loading documents
doc = []
#list of files
filenames = os.listdir("Doc50")
for i in range(50):
with open("Doc50/"+filenames[i], 'r') as documents:
doc.append(documents.read())
documents.close()
#==============================================================================
# for i in range(0, 50):
# doc[i] = re.sub('[^a-zA-Z]', ' ', doc[i])
# doc[i] = doc[i].lower()
# doc[i] = doc[i].split()
# ps = PorterStemmer()
# doc[i] = [ps.stem(word) for word in doc[i] if not word in set(stopwords.words('english'))]
# doc[i] = ' '.join(doc[i])
#
#==============================================================================
#==============================================================================
# #Bag of words feature extraction
# count_vect = CountVectorizer(ngram_range = (1,3), encoding='utf-8',decode_error='replace',strip_accents='unicode'
# ,analyzer='word',max_df= 0.25, min_df = 7, max_features = 25)
# count_vectorized = count_vect.fit_transform(doc)
#
# count_vocab = count_vect.get_feature_names()
#==============================================================================
#==============================================================================
# from sklearn.decomposition import TruncatedSVD
# svd = TruncatedSVD(n_components=1, n_iter=15, random_state=0)
# svd.fit(count_vectorized)
# svd.transform(count_vectorized)
#
#==============================================================================
#Tfidf features
tfidf_vect = TfidfVectorizer(ngram_range = (1,3),norm='l2',smooth_idf = False , analyzer='word', max_df= 0.25,min_df = 9, stop_words = 'english')
tfidf_vectorized = tfidf_vect.fit_transform(doc)
count_vocab = tfidf_vect.get_feature_names()
#Stacking
#count_vectorized = hstack((count_vectorized,tfidf_vectorized))
count_vectorized = tfidf_vectorized
#cluster range
range_n_clusters = [5]
for n_clusters in range_n_clusters:
#initializing models
clusterer_k = KMeans(n_clusters=n_clusters, random_state=99)
clusterer_a = AgglomerativeClustering(n_clusters=n_clusters)
#clusterer_b = BisectKMeans(n_clusters=n_clusters)
#fitting and predicting the clusters
cluster_labels_k = clusterer_k.fit_predict(count_vectorized.toarray())
cluster_labels_a = clusterer_a.fit_predict(count_vectorized.toarray())
#cluster_labels_b = clusterer_b.fit_predict(count_vectorized.toarray())
#Calculating Silhouette average scores for each cluster
silhouette_avg = silhouette_score(count_vectorized, cluster_labels_k)
print("For n_clusters =", n_clusters,
"The average silhouette_score_k is :", silhouette_avg)
silhouette_avg = silhouette_score(count_vectorized, cluster_labels_a)
print("For n_clusters =", n_clusters,
"The average silhouette_score_a is :", silhouette_avg)
gt = []
gt += 10 * [4]
gt += 10 * [0]
gt += 10 * [1]
gt += 10 * [2]
gt += 10 * [3]
gt = np.array(gt)
print(purity_score(cluster_labels_k,gt))
print(purity_score(cluster_labels_a,gt))
sorted_index_centroids = clusterer_k.cluster_centers_.argsort()[:,
::-1]
for i in range(5):
print("Cluster %d words:" % i, end='')
#replace 10 with n words per cluster
for ind in sorted_index_centroids[i, :3]:
print(' %s' % count_vocab[ind].split(' ')[0], end=',')
print() #add whitespace
print() #add whitespace
#==============================================================================
# silhouette_avg = silhouette_score(count_vectorized, cluster_labels_b)
# print("For n_clusters =", n_clusters,
# "The average silhouette_score_b is :", silhouette_avg)
#==============================================================================
#Computimg the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(count_vectorized, cluster_labels_a)
y_lower = 10
for i in range(5):
ith_cluster_silhouette_values = \
sample_silhouette_values[cluster_labels_a == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
sorted(filenames)
sorted(cluster_labels_k)
new_dict = dict(zip(filenames, cluster_labels_k))
print(new_dict)